Human functional population genomics has great potential to increase our understanding of biological mechanisms that link genetics with complex human disease. Many genetic variants associated with human disease influence mRNA levels, and, it is assumed, protein expression variation. We believe that collection of proteomic data from samples of the Genotype-Tissue Expression (GTEx) program will complement other ongoing genomic data collection efforts. Through direct measurement of protein levels across individuals and tissues, we will have the ability to further validate functionality of genome-transcriptome relationships while simultaneously characterizing novel genome-proteome relationships and their relationship to transcriptome biology, and at the same time characterizing the tissue-context specificity of these relationships. Knowledge of the unique relationships between genomes with proteomes and transcriptomes will allow us and others to subsequently explore novel hypotheses related to the genetic components of common diseases. We will characterize five tissues of the GTEx samples in a high-throughput, robust manner for protein levels in a population-based framework, and will analyze these data in the context of additional GTEx genomics datasets, and publically available genome annotation. The research will build upon existing resources, including a core proteomics facility, robust analytic pipelines, high performance computing, but more importantly on our long- standing research interests in human population genomics, discovery and characterization of regulatory variation, and genome studies of complex diseases and traits in humans. Thus, our specific aims are: 1) to characterize protein expression profiles across five human tissues to discover cross-tissue and tissue-specific protein expression variation for individual proteins and pathways; 2) to integrate the relationships between variation in genetic, transcriptome and proteome profiles; and 3) to use systems and network approaches for a better understanding of the organization of the proteome and transcriptome, including regulatory circuits within and across tissues. All data and results produced through this project will be made publicly available immediately for use by the larger scientific community.